June 24 - 26, 2018
Loews Atlanta Hotel, Atlanta, GA

Refining Organizational Finance with Data and Analytics

Refining Organizational Finance with Data and Analytics

KhozemaShipchandler | VP & CFO | GE Digital

Prior to discussing the topic in greater detail at the upcoming Chief Financial Officer Exchange, GE Digital’s Vice President and CFO, Khozema Shipchandler discusses GE’s reasoning for an expansive overhaul of its executive training program. In this Q&A, he touches on a few of the challenges of instilling the importance of data analytics capabilities and some of the solutions they found.

What fueled GE’s decision to overhaul its executive training programs? 

Khozema: Today’s world is moving quickly. In parallel, companies like GE have become more digital – that is, less dependent on paper – and started to amass large amounts of data. However, a mechanism to get that data into the hands of our leaders in order to enable them to make strategic, effective and timely decisions was still missing. And the longer leaders depend on instinct and experience, instead of data-driven insights, the harder it is to change the way they work. We realized that we had to train GE’s next generation leaders on how to link strategy, planning, execution and reporting through the use of data and to become fluent in contemporary techniques when generating insights from that data.

What are some of the challenges that you had to overcome? Were any unforeseen? 

While we’ve been investing in data analytics capabilities for years, mass adoption of the tools and techniques being developed has been low. This was primarily driven by two causes:

  1. People don’t always have an understanding of the art of the possible. Having spent time in Boston at places like MIT’s Media Lab seeing bleeding-edge research, in San Francisco meeting with entrepreneurs and at startups, and then even going as far as Shanghai and seeing how Chinese companies like Ali Baba are driving technology adoption across hundreds of millions of users, myself and my leadership team had an understanding of the massive opportunity that data, analytics, and technology presented. But we still had to make it real for the broader organization.

  2. We usually live our life jumping from meeting to meeting. In the limited downtime between these face-to-face sessions, we’re emailing files, opening them in spreadsheets, copying data into a slideshow presentation, and then bringing that to our next meeting. It’s an archaic way of working, but the reality is that people don’t have the time, or more importantly, the incentive, to break from this repetition and try something new.

What are some of the best solutions to those types of challenges? 

Khozema: Broadly, staying in touch with the organization helps, as the challenges we faced aren’t unique to us, but also not the definitive list. However, to address the two above points:

  1. We built out a dedicated Data Analytics team – with experience anywhere from biomedical engineering to credit risk analysis – who knew the power of data. Then we put them to work on problems that our teams in the field deal with every day. And then we shared the results with the organization. Contract reviews in our Aviation business typically take 2 weeks for a single associate; the team used natural language processing and machine learning to bring that down to a matter of minutes. T&L audits generally yielded limited results; the team used a regression tree and clustering to yield a 60% hit rate on potentially fraudulent transactions. When the organization realized that data and analytics could present an opportunity to work faster, with better results, it was a no-brainer.

  2. Incentive drives behavior. So we created basic incentives. Every time we had a review with the staff, we asked how they performed the analysis and then challenged if analytics weren’t being used. Every time someone presented work to us in PowerPoint, we asked why not Tableau or Spotfire. Every time we publically recognized top performers in the organization, we highlighted their use of data analytics. Very quickly, the organization of high performers got the point, and adoption started to increase. But this was just a growth hack – as the teams started immersing themselves in these tools and techniques, they learned the value for themselves, and over time it became self-sustaining.

Utilizing analytics and machine learning allows organizational finance to act quicker, cover more data, and generate new and improved insights.

What aspects of the Chief Financial Officer Exchange are you most looking forward to? 

Join Khozema on day 2 for his session: CFO of the Future: The Digital Financial Leader

Download the Agenda for more information.